Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

★★★★★ 4.7 123 reviews

$61.59
Price when purchased online
Free shipping Free 30-day returns

Sold and shipped by comt.myasdf.us
We aim to show you accurate product information. Manufacturers, suppliers and others provide what you see here.
$61.59
Price when purchased online
Free shipping Free 30-day returns

How do you want your item?
You get 30 days free! Choose a plan at checkout.
Shipping
Arrives Jun 29
Free
Pickup
Check nearby
Delivery
Not available

Sold and shipped by comt.myasdf.us
Free 30-day returns Details

Product details

Management number 231713179 Release Date 2026/06/18 List Price $24.64 Model Number 231713179
Category

Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance. Read more


Correction of product information

If you notice any omissions or errors in the product information on this page, please use the correction request form below.

Correction Request Form

Customer ratings & reviews

4.7 out of 5
★★★★★
123 ratings | 50 reviews
How item rating is calculated
View all reviews
5 stars
86% (106)
4 stars
2% (2)
3 stars
1% (1)
2 stars
1% (1)
1 star
10% (12)
Sort by

There are currently no written reviews for this product.